IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1
Towards Visualization Thumbnail Designs that
Entice Reading Data-driven Articles
Hwiyeon Kim, Joohee Kim, Yunha Han, Hwajung Hong, Oh-Sang Kwon, Young-Woo Park,
Niklas Elmqvist, Senior Member, IEEE, Sungahn Ko, and Bum Chul Kwon
Abstract—As online news increasingly include data journalism, there is a corresponding increase in the incorporation of visualization
in article thumbnail images. However, little research exists on the design rationale for visualization thumbnails, such as resizing,
cropping, simplifying, and embellishing charts that appear within the body of the associated article. Therefore, in this paper we aim to
understand these design choices and determine what makes a visualization thumbnail inviting and interpretable. To this end, we first
survey visualization thumbnails collected online and discuss visualization thumbnail practices with data journalists and news graphics
designers. Based on the survey and discussion results, we then define a design space for visualization thumbnails and conduct a user
study with four types of visualization thumbnails derived from the design space. The study results indicate that different chart
components play different roles in attracting reader attention and enhancing reader understandability of the visualization thumbnails.
We also find various thumbnail design strategies for effectively combining the charts’ components, such as a data summary with
highlights and data labels, and a visual legend with text labels and Human Recognizable Objects (HROs), into thumbnails. Ultimately,
we distill our findings into design implications that allow effective visualization thumbnail designs for data-rich news articles. Our work
can thus be seen as a first step toward providing structured guidance on how to design compelling thumbnails for data stories.
Index Terms—Data journalism, data-driven storytelling, online news, visualization, thumbnail images, data stories.
1 INTRODUCTION
T
HUMBNAILS are small and static images that accom-
pany titles and bylines in documents [1], [2], [3]. News
organizations have extensively used thumbnails for their
online news articles to induce reader attention and attract
clicks. As data stories—where visualization plays an im-
portant role—grow in popularity, news organizations are
starting to publish thumbnails that incorporate visualization
into the thumbnails. These visualization thumbnails are differ-
ent from conventional thumbnails because they have great
potential to both convey an article’s main message using its
embedded visualization as well as entice the reader to click
on the article link based on the appeal of the displayed data.
Several news media outlets have begun to adopt such
visualization thumbnails. For example, The New York Times
presents many visualization thumbnails in its data story
dedicated section “The Upshot." FiveThirtyEight showcases
many visualization thumbnails that can effectively catch the
reader’s attention. Although many types of visualization
thumbnails have been designed, few studies have inves-
tigated the variety of design choices. As a result, many
questions remain unanswered about optimal visualization
thumbnail design. For example, “The Upshot” and The
Economist tend to use thumbnails that are resized from
visualizations embedded in the article. Meanwhile, The Pud-
ding, FiveThirtyEight, and The Wall Street Journal often design
visualization thumbnails with editing choices different from
H. Kim, J. Kim, Y. Han, O. Kwon, Y. Park, and S. Ko (corresponding
author) are with UNIST, Ulsan, South Korea.
H. Hong is with KAIST, Daejeon, South Korea.
N. Elmqvist is with the University of Maryland, College Park, MD, USA
B.C. Kwon is with IBM Research, Cambridge, MA, USA
Manuscript received XX YY, 2020; revised AA BB, 2021.
the source visualizations in the article by adding annotations
or removing axes. However, it is unclear which factors
influence both interpretability—helping readers understand
the article from the thumbnail alone—and appeal—inducing
the reader to click to read the article.
In this research, we aim to investigate visualization
thumbnail designs that support both of these affordances.
To this end, we first survey existing visualization thumb-
nails from news organizations and find different types of
thumbnail designs (Section 3.1). Then we interview six
industry practitioners to understand their thumbnail design
strategies and intentions (Section 3.2). With the survey and
interview results, we conduct a user study (n = 161)
using 16 thumbnails that we carefully design with industry
practitioners to investigate (RQ1) visualization thumbnail
designs that readers want to see, and (RQ2) the roles of
thumbnail components (Section 4). The results indicate that
readers want visualization thumbnails that quickly capture
their attention and effectively convey the articles’ main
point (Section 5). We report the lessons learned and design
implications derived from the study and discuss open re-
search areas on visualization thumbnails (Section 6.1). To
our knowledge, this is the first attempt to explore the design
space, component roles, and readers’ choice of visualization
thumbnails with evaluation.
This paper is a significantly extended version of a IEEE
VIS 2019 short paper surveying visualization thumbnails for
data stories [4]. Compared to this earlier report, this paper
includes empirical evaluation involving 161 crowdworkers
as well as our corresponding analysis and findings. The
supplemental material for this journal version of the work
can be found on OSF: https://osf.io/khgw2/
The main contributions of this work are as follows: (1)
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2
Extracting the key components of visualization thumbnail
designs by surveying in-the-wild visualization thumbnails;
(2) identifying reader preferences for visualization thumb-
nail designs and their rationale for their preferences via
a crowdsourced user study; and (3) reporting lessons, de-
sign implications, and open research areas for visualization
thumbnails.
2 RELATED WORK
We situate our work in relation to thumbnail design and to
the use of visualization for communication, particularly in
data journalism.
2.1 Thumbnail Design
Prior research has shown that thumbnails help people locate
and rediscover content during web searches, media brows-
ing, and sensemaking. For example, when searching the
web, thumbnails paired with titles, text snippets, and URLs
help people find articles of interest online [2], [5], [6]. Aula
et al. [2] show that image thumbnails add information
about the relevance of the web page compared to the text
summary. Thumbnails also appear when browsing other
types of media such as file systems [7], documents [8],
and videos [9]. When an email contains a link to a video
file, Topkara et al. [10] finds that more people click on the
link displayed with the thumbnail. Previous work on visu-
alization and visual analysis has incorporated thumbnails
into the sensemaking process as a means of leveraging the
unique advantages of spatial memory [11], [12], [13].
In this study, we investigate the role thumbnails play
as people browse online news articles in the context of
data journalism. On online news webpages, thumbnails of
articles must compete for the readers’ attention with one an-
other and with other content. There are several factors that
affect the interest and ultimate usefulness of a thumbnail,
such as the size of the thumbnail and the text it contains.
For example, Kaasten et al. [14] conclude that thumbnails
must be larger than 96 × 96 pixels to trigger recognition
among returning readers to pages with multiple thumbnails.
Woodruff et al. [6] also find that enhanced versions of
thumbnails including keywords have better search perfor-
mance than text summaries or image thumbnails.
Previous studies have been conducted to automatically
generate image thumbnails by cropping, reducing, or se-
lecting salient parts of photography included in articles [1],
[15], [16]. Song et al. [3] present an algorithm for selecting
the most salient and evocative thumbnail among image
candidates to represent a video. Compared to this prior art,
our study is the first to study visualization images, as well as
the first step towards the automatic creation of visualization
thumbnails based on a design space for data journalism.
2.2 Storytelling in Thumbnails
Thumbnails can be a key player in visual communication
and storytelling [17], [18], as their innate goal is to effectively
convey a story in a concise visual format. But as they are
usually given a smaller space budget, there have been many
discussions on “how much is much” (i.e., a debate between
minimalism and chartjunk). Many visualization researchers
have conventionally supported minimalism in visualization,
stating “less is more,” as they believe that objective charac-
teristics in visualization improve visualization performance.
Edward Tufte [19], one of the most vocal proponents of
maximizing the “data-to-ink” ratio, argues that “ink that
fails to depict statistical information does not have much
interest to the viewer.”
There are researchers who argue against minimalism in
visualization, stating that complexity (e.g., chartjunk [20])
is not a bad thing and could make a design more attrac-
tive [21]. Many experiments have been performed over the
years, but results seem to support that “chart junk” or
embellished graphics (e.g., Nigel Holmes’ work) is not a bad
thing. Kelly [22] conducted such an experiment and found
that 120 participants made similar errors in comprehension
and search tasks with the infographics data (collected from
USA Today), regardless of data-ink ratios. Other researchers
also observe in their experiments that visual embellishment
does not have a significant impact on visualization perfor-
mance [23], [24]. Rather, visual embellishment even results
in better results in supporting human recall ability [20], [25],
[26], [27] and qualitative dimensions (e.g., aesthetics and
preference) [28], [29]. Due to the importance of annotations
(i.e., explanation text in this work) in story-telling, which is
also shown in our study, Ren et al. [30] propose a visual tool
for efficiently producing annotations, which can be greatly
useful for making thumbnails with ample context.
Annotations or other visual components may carry a bias
or slant [31]. Whether intended or not [32], a spotted bias
on thumbnail’s annotations negatively impacts an article’s
credibility and legitimacy. Kong and Agrawala [33] show
that placing additional layered information could aid chart
reading. We report how visual components on visualization
thumbnails can impact article reading and credibility.
2.3 Visualization in Data Journalism
With data journalism on the rise, visualization is becom-
ing pervasive in news media [34]. Different intentions for
visualization communication often lead to different design
choices [35], encountering substantial use of graphical and
text-based annotation [30]. We see that some visualizations
incorporate human-recognizable objects [36], such as icons
and logos, to help people understand the data. Other graph-
ics that are not directly relevant to data [37], such as portraits
and illustrations, are also incorporated into visualizations
and considered embellishments [20], [23] to enhance the
meaning of the chart. These graphical annotations are con-
sidered aesthetic design choices [24] that make positive
first impressions [38] on the reader. However, there is still
debate as to whether these decorations have a positive effect
on the informativeness of charts. While some studies have
shown that embellishments can increase comprehension [33]
and memory [25], [26], [27], Hullman and Adar [18] argue
that some embellishments make charts more difficult to
interpret. Andry et al. [39] quantitatively measure people’s
understanding of chart embellishments and show that there
is a positive effect within limited boundaries.
Since online news articles must hook users in a limited
time [40], the design of the first impression [38] is vital.
Transparency of visualization is consistently emphasized
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 3
as a challenge in data journalism [41]. Some practices of
generating slanted titles [31] or designing deceptive visual-
izations [32] can bias and often mislead readers. Designing
thumbnails with visualizations is also important because the
thumbnails represent their articles and provide first impres-
sions for the articles. Therefore, there is a need for research
on how to design attractive and effective thumbnails that
can be easily interpreted without misleading the reader.
Building on the above understanding, we further explore
the current practices of visualization thumbnail design.
3 VISUALIZATION THUMBNAIL DESIGN PRACTICE
To our knowledge, prior research has yet to examine the
design of thumbnails in data journalism, and particularly
those that we refer to as visualization thumbnails. To better
understand current practices in visualization thumbnail de-
sign, we collected examples from data journalism outlets
and conducted semi-structured interviews with six graphic
designers working for news websites.
3.1 A Survey of Visualization Thumbnails
We began by collecting news articles published between
November 1, 2018 and December 31, 2018 from online news
organizations reputed for their data journalism, the Pew Re-
search Center (Pew.), The Economist (Eco.), The New York Times
(NYT), including The Upshot and DealBook, FiveThirtyEight
(538), The Wall Street Journal (WSJ), First Tuesday Journal
(1st), and Bloomberg News (BBG). We concentrated on articles
relating to politics and economics, as these topics tend to
use visualization to a greater degree than others covered by
these news outlets. This initial corpus contained 139 articles
that include visualizations within the article body. Among
these, 48 articles did not feature visualizations in the article’s
thumbnail, instead opting for photographs and other visual
imagery aside from visualization. Of the remaining 91, we
decided to focus on basic charts, such as bar and line
charts and scatterplots, because they are most common in
news media. Thus we excluded 24 articles whose charts are
not axis-based (e.g., maps) or are infrequently used (e.g.,
pictogram charts, Sankey diagrams).
Among the remaining 67 articles, 39 included a visual-
ization used in the body of the article as its thumbnail, re-
ducing its size or cropping it. Thumbnails for the remaining
28 articles modified a visualization used in the body of the
article in some way (e.g., omitting axes). Examining these
67 thumbnails further, we codified 1) which visualization
components were modified (e.g., an axis); and 2) how com-
ponents were modified (e.g., omitted). Three authors of this
paper independently codified these components and later
merged their codes in an iterative discussion, arriving at
96% agreement (Fleiss’ Kappa = 0.75).
To label the components, we initially considered using
existing classifications, namely Borkin et al.’s classification
of visualization components [27], Byrne et al.’s [37] distinc-
tion between graphical and figurative components [37], and
Ren et al.’s [30] classification of annotation. We realized that
these classifications were insufficient in isolation in terms
of capturing all aspects of visualization thumbnail design.
For example, Borkin et al.’s classification defines “text” as
100b
80b
60b
40b
20b
0b
03 05 07 09 11 13 15 17 19
Market capitalization (in billions)
Apple vs. Microsoft
Apple
Microsoft
$67b
the gap between companies
continued to grow
Apple
Microsoft
Chart Title
HRO
HRO
Data Label
Y Axis Label
X Axis
Highlight
Exp. text
Legend (Explicit)
Legend
(Implicit)
Y Axis Title
X Axis Title
Years
Fig. 1. Line chart annotated (in red) according to our classification
of chart elements. The chart features examples of additional compo-
nents including HROs and highlights, as well as basic components such
as axes, data labels, and legends.
Visualization in an article
Resized
Cropped
Modied
Fig. 2. Generation strategies. Many news outlets generate thumbnails
by resizing, cropping, and modifying original visualizations in articles.
“any text in the image,” so axis titles, annotations, and
captions fall under the same category. Meanwhile, Byrne
et al.’s classification provided broad categories for coding
figurative components. Finally, Ren et al.’s classification
could not be used to describe chart components beyond an-
notation. While these classifications informed our analysis,
we struggled to use them as a means to codify the designers’
intentions or goals. We therefore combined and extended
the aforementioned classifications, resulting in a new classi-
fication having categories that explicitly acknowledge each
visualization element’s role.
As a result, we identified 14 basic and 4 [R2-18]additional
component types. The basic component types refer to chart
components that directly facilitate chart reading and under-
standing (e.g., two axes in the line chart). Figure 1 shows
examples of the basic component types. They include: x-
and y-axes with labels, tick marks, data labels, chart titles,
and legends. By data labels, we mean any text that directly
reflects a data value (e.g., ‘$67b’ in Figure 1). We further dis-
tinguish two types of legends: explicit legends: those drawn
in a dedicated area; and implicit legends: those drawn directly
within the visualizations (e.g., Apple & Microsoft in Fig-
ure 1). Additional components include explanatory text (or
exp. text), highlights (e.g., the vertical arrow in Figure 1, the
red bar in this thumbnail [42], Human Recognizable Objects
(HROs) [27]), and Graphics Not Relevant to Data (GNRD)
to capture all forms of graphical embellishment. HROs are
pictorial components used in legends (e.g., the Apple and
Microsoft logos in Figure 1 and a small human object in
this thumbnail [43] or to encode data points [36]. GNRDs
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 4
TABLE 1
Our classification of 67 visualization thumbnails. Filled values reflect
modifications from a visualization found in the article. An interactive
version of this table can be accessed at
http://hci.unist.ac.kr/SurveyVisThumbnails/
Removed Added Remained Editing Type
538
Pew.
Eco.
NYT
WSJ
WSJ
BBG
BBG
BBG
1st
538
538
Pew.
Eco.
Eco.
Eco.
Eco.
Eco.
NYT
NYT
NYT
SUM
Y Axis Label
Y Grid Line
X Axis Label
Chart Title
Legend Implicit
Y Axis Title
X Axis
X Tick Marks
Legend Explicit
X Grid Lines
Data Label
Y Axis
Y Tick Marks
X Axis Title
Exp. text
GNRD
HRO
Modified
Cropped
Resized
SUM
Highlight
Pict.
DP Scatterplot
Bubble
Bar + Line Bar Line
BASIC CHART COMP. ADDED CHART COMP.
48
42
41 31 29 27 26 24` 21 1214 10 9 2 36 36 6 3 2829 18
8
3
3
3
1
4
10
8
8
7
5
5
9
9
9
8
8
8
8
8
7
7
7
6
6
5
5
5
9
7
4
6
3
4
2
5
4
3
3
9
8
8
8
6
5
7
5
2
9
8
4
5
2
7
3
3
9
9
9
1
7
5
8
10
10
10
10
Fig.1&
Article 3
Fig.3
Article 1
Article 2
Fig.2-right
Fig.2-left
are images or illustrations that reflect the article’s context
but are not directly related the data, such as the blue image
(bottom-right) in this thumbnail [43]. Figure 2 describes the
current thumbnail creation strategies of news organizations
(e.g., resized, cropped, and modified thumbnails).
Table 1
1
presents the result of coding 67 visualization
1. Online version: http://bitly.kr/AGRYQA
TABLE 2
Thumbnail classification. 24 visualization thumbnails that are not
axes-based (e.g., maps) or are infrequently used.
Modified
Cropped
Resized
Map Pie PCP San.
line+
map
bar+
line+
map
Rad.
BBG Pew. Eco. NYT WSJ FTJ NYT 538 Eco. Eco.BBG Pew.
SUM
8
14
3
thumbnails
2
using our basic and additional component clas-
sification, along with our modified / resized / cropped dis-
tinction; we also indicate chart types and sources along the
table’s vertical axis. ‘DP’ and ‘Pict.’ in the first column (i.e.,
chart type) corresponding to dot plots and pictograms chart,
respectively.
Our codification suggests several trends. Thumbnails for
line charts (30 out of 67) tend to omit the X-axis title, the Y-
axis, and legends. However, they tend to include additional
components such as highlights and explanation text. Bar
charts and scatterplots tend to include diverse combinations
of components that are omitted or added in thumbnails.
Example components used for the combinations include X-
axis grid lines, titles, and Y-axis. We are also able to contrast
the strategies of different news organizations. For instance,
FiveThirtyEight tends to remove nearly all components from
line charts in thumbnails; they also tend to add GNRDs
and HROs (e.g., [44]). Meanwhile, traditional print media
organizations such as The New York Times and The Economist
tend to crop or resize charts when producing thumbnails.
Many media organizations seem to prefer modification (28)
and crop (29) strategies in their designs. In particular, we see
that FiveThirtyEight and the Pew Research Center are more
inclined to modify existing visualizations, while the New
York Times seems to prefer cropping existing visualizations
embedded in articles. Lastly, we see considerable variability
in Table 1 which is an indication of a need for greater
understanding in terms of how visualization thumbnail de-
sign choices affect readers’ interpretation as well as readers’
likelihood to read the article.
Table 2 shows the codification results of the editing
strategies (i.e., modified, cropped, or resized) of 24 visu-
alization thumbnails, including maps, pie charts, parallel
coordinates, radial column charts (Rad.), Sankey diagrams
(San.), and composite charts. We coded the editing strategies
separately for the additional visualizations because those
strategies can be applied to any visualizations. As seen in
Table 2, cropping is more popular than the other two strate-
gies for the additional 24 visualizations; however, cropping
is not especially popular in the 67 visualizations, which
include X-axis and Y-axis, as shown in Table 1. Most maps
are presented as cropped, losing chart titles or legends. In
analyzing the thumbnails, such a removal seemed effective;
it allows more space without causing additional difficulty
in recognizing color-coded legends and small text. The
discussed trends in this section are examples and by no
means comprehensive.
2. Thumbnail collection: http://bitly.kr/bibRs4
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 5
3.2 Interviews with Visualization Designers
To understand the current practices in visualization thumb-
nail design further, we engaged in informal interviews with
six visualization designers from our extended professional
networks who are employed by news media organizations.
These included two journalist-engineers (with 6 and 16
years of experience, respectively), an interactive graphics
developer (with 15 years of experience), a senior news
article editor (with 12 years of experience), a data scientist
(with 6 years of experience), and a computational journalist
(with 3 years of experience). Given the demanding nature of
their work and the time zone differences between us, these
interviews were asynchronous and occurred via email or
within the #journalism channel of the Data Visualization
Society’s Slack workspace.
3
We asked these practitioners about two topics: (1) their
intentions with respect to designing thumbnails for articles
that prominently feature visualization; and (2) the chal-
lenges of incorporating visualization into article thumb-
nails. These practitioners reported a broad set of goals for
thumbnails. First, their thumbnails must build and reinforce
the organization’s brand identity. This requirement often
constrains the choice of colors, font types, and HROs used in
thumbnails. Second, their thumbnails must be aesthetically
appealing in order to draw readers’ attention, particularly
when the thumbnail appears in a visually rich social media
feed. Third, their thumbnails must also reflect any unique
artwork or visual content commissioned for the article (if
applicable), which might include illustrations, collages, or
animations.
In regards to how they design thumbnails for data
stories, these practitioners indicated that there are no hard
and fast rules of thumb that can be applied to all cases.
Aside from using a limited color palette to reinforce brand
recognition, two of these practitioners admitted to avoiding
the use of visualization in thumbnails. Instead, they opted
to incorporate photographic imagery whenever possible,
and that photos of people appear to drive more traffic to
articles. Maps also appear to successfully drive readers to
articles. One practitioner reported that visualizations are
often regarded by readers as cold, intimidating, or inacces-
sible,” and that while a visualization in isolation does not
communicate much in itself, they can sometimes be used
to accentuate a photographic or illustrated thumbnail. For
instance, a composite thumbnail can incorporate a photo
of a person with a semi-transparent visualization overlay.
This practitioner also avoided incorporating visualization
in thumbnails because they did not want to give away too
much content before the reader arrives at the article. Lastly,
we also learned that creating a thumbnail for an article is
often not the responsibility of the article author or visual-
ization designer. Instead, thumbnail design is designated
to designers and social media content producers who are
typically not involved in writing the article.
3.3 Visualization Thumbnail Definition and Goals
Based on the survey results and our interviews, we define
a visualization thumbnail as a thumbnail that includes one or
3. https://www.datavisualizationsociety.org
more visualizations. A thumbnail, in turn, is a small image
typically between 96 × 96 and 256 × 256 pixels in dimen-
sion designed as a preview of a larger one. Visualization
thumbnails are commonly designed to be informative to
allow readers to understand the gist of an article without
reading, as well as enticing readers to click the article for
further reading. This does not imply that a thumbnail must
be inviting and informative to be considered a visualiza-
tion thumbnail; only that if both of these design goals are
satisfied, the thumbnail can be considered a well-designed
visualization thumbnail.
Two perspectives of this definition exist, and each per-
spective leads to different visualization thumbnail design
goals. The goal of the professional visualization thumbnail
designer is to draw readers’ attention to the thumbnails and
to increase the click rate of the article per exposure. This
means that their design goals for a visualization thumbnail
goals are not much different from those of normal thumbnail
images. Therefore, conventional design goals, such as deter-
mining an image’s attractiveness [3] or visual aesthetics [38],
[45], [46], also apply to visualization thumbnail evaluation.
However, the goal of the consumer is to use the visu-
alization thumbnail to select articles that best match his or
her subjective criteria (e.g., preferences, interests, or inten-
tions). For these users, informativeness in thumbnails can
be the most important requirement that allows the readers
to quickly and accurately judge whether the thumbnail’s
associated article meets their selection criteria. This perspec-
tive leads to further design goals, such as informativeness,
relevance, interpretability, and straightforwardness [1], [2],
[6], [47], [48].
In an ideal scenario, the goals of producers and con-
sumers for visualization thumbnails are the same. However,
in our interviews, practitioners with extensive experience
asserted that there are cases in which they do not design
visualization thumbnails to simply meet readers’ needs
because they also need to consider user click counts.
In this paper, we seek to understand how to design vi-
sualization thumbnails to merge both of these perspectives.
More specifically, we are looking for a middle ground where
visualization thumbnails are both visually appealing—thus
inviting more clicks—as well as representative of the under-
lying article—thus ensuring that readers do not feel misled.
We believe our work to be the first investigation of this topic.
Due to the limitations in the existing methods and scopes of
the study, the proposed definition should be considered as
a working definition and could evolve or be replaced with
the results of follow-up experiments and studies.
Though our interviews with practitioners were informal
and by no means exhaustive, we were encouraged to learn
about the lack of consensus in regards to guidelines for
visualization thumbnail design. We therefore remained cu-
rious about what makes visualization thumbnails effective,
or how their components contribute to whether readers find
them to be inviting and informative. Conversely, we also
questioned which thumbnail components could be used
to mislead or misinform readers. For example, a photo of
United States special prosecutor Robert Mueller looking
down may deliver a feeling of disappointment to readers.
Considered alongside our survey results, these interviews
reinforced the need for further empirical study.
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29 participants
GNRD
LeBron
Trump
Apple
vs.
Microsoft
Kavanaugh
53 participants
Highlight
52 participants
HRO
27 participants
Resized
Fig. 3. Four types of visualization thumbnails. 16 thumbnails of four types with four articles from different topics that are used in our experiment.
Columns ( , , , ) refer to thumbnail types, and rows (LeBron, Trump, Apple vs. Microsoft, Kavanaugh) refer to article types.
4 USER STUDY
Our survey and interviews revealed a lack of studies on
reader preferences for visualization thumbnail design. To
investigate these, we formulated two research questions
regarding (RQ1) what design types readers would most likely
click to read and why?, and (RQ2) what do readers think of
the components in visualization thumbnails? To answer these
questions, we conducted an online user study.
4.1 Visualization Thumbnail Design
There are three considerations for designing the thumbnails
for this study, including chart and editing types and com-
ponent combinations. In terms of chart type, we use the line
chart due to its popularity (Table 1) and interpretability [49].
Since we limit our discussion to the line chart thumbnails,
there remains a research question on extending the design
space for charts with different visualization components,
such as bar, bubble, or scatterplot, and maps. For the editing
type, we exclude cropped thumbnails because they could
cause unexpected issues, such as deception [32], [50], [51].
We initially considered a design with many components and
their combinations for RQ2, but soon found that there were
too many permutations (e.g., 262,144 combinations with 18
chart components), making a user study infeasible without
any guidelines for component selection.
We therefore design our visualization thumbnails based
on the chart component survey result [4], including the
basic chart components. We also include GNRDs (Graphics
Not Related to Data) [20], [25], [26], [27], [28], [29], HROs
(Human-Recognizable objects) [27], [36], titles, and visual
highlights with data labels [18], [52], [53], all of which affect
the appeal and interpretability of a thumbnail.
We select four articles for the study based on four con-
siderations; thumbnails should 1) be easy for a layperson to
read and understand; 2) include at least one visualization
that conveys the article’s main point; 3) have a visualization
for two data series that can be represented as icons or logos
to help readers distinguish two or more pieces of data;
and 4) contain the main character that can be displayed as
GNRDs. After a careful examination of candidate articles,
we find four articles that satisfy our requirements. The
topics of the four articles pertain to sports star’s influence
on fan bases [54] (LeBron), a recent U.S. president’s approval
ratings [55] (Trump), a comparison of Apple and Microsoft
stock prices [56] (Apple vs. Microsoft), and a summary
judgment of a politician’s confirmation [57] (Kavanaugh).
Among the four articles, the articles about Trump and Apple
vs. Microsoft contain only one visualization in each article.
The articles about LeBron and Kavanaugh include more
than two visualizations. When designing our visualization
thumbnails for the experiment, we referred to articles’
thumbnails or the visualization most discussed in the article.
Surveying visualization thumbnails with two experts in
the field of design, we find that thumbnails have many
design variables in the thumbnails that designers manip-
ulate to create strong impressions of brand identity and
invite more readers to click. These design variables include
colorful backgrounds [58], unique layouts (e.g., hiding chart
areas by a figure: [44]), and images of faces with stimulat-
ing facial expressions and gestures (e.g., a photograph of
Donald Trump: [59]). Due to its large decision space and
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 7
TABLE 3
Chart components. Identified for each thumbnail.
Y Axis Label
Y Grid Line
X Axis Label
Chart Title
Legend Implicit
Y Axis Title
X Axis
X Tick Marks
Legend Explicit
X Grid Lines
Data Label
Y Axis
Y Tick Marks
X Axis Title
Exp. text
GNRD
HRO
Modified
Cropped
Resized
SUM
Highlight
BASIC CHART COMP. ADDED CHART COMP.
8
2
4
5
Removed Added Remained Editing Type
GNRD
Highlight
HRO
Resized
possible bias, we decide to use fixed values for the variables.
For example, we use the same color sets with original
visualization, the same layouts for the same thumbnail type,
and figures with neutral facial expressions.
Based on the considerations and articles, we then present
the designed visual thumbnails. We design 16 thumbnails of
four types, as shown in Figure 3. Table 3 summarizes how
we control the chart component combinations. To decide the
size of the visualization thumbnails, we survey the size of
visualization thumbnails from news organizations (supple-
mentary material https://osf.io/khgw2/). Among different
thumbnail sizes, we choose Pudding’s size (width: 343px,
height: 193px), because its width is close to an average (i.e.,
not extremely small or large), and it can sufficiently show all
components of the designed thumbnails in popular mobile
devices (e.g., iPhone X, display size: 375×812px).
thumbnails are the resized versions of the
visualizations from the original articles. The New York Times
and The Economist frequently use this design approach.
The charts in this type tend to contain many components,
including basic chart components, such as axis labels, im-
plicit legends, and a chart title, as many other thumbnails
do (e.g., [56]). There could be minor differences in axis
representations (e.g., tick marks) and the use of grid lines
in the original visualizations in the articles. For consistency
with thumbnails, we use the same style of tick marks
and gridlines across the thumbnails.
thumbnails consist of a photograph (GNRD) and
a simplified chart without any axis labels. GNRDs are
included in the thumbnails to invite readers, stimulating
their curiosity (e.g., the photography in this thumbnail of a
woman walking: [60]). They often imply information about
an article’s content by showing a person who is related to
the content. In general, the chart shows an overall trend
without axes, and words as implicit legends. This type
of thumbnail is frequently shown in FiveThirtyEight’s (e.g.,
[44]) and The Wall Street Journal’s graphics.
The main feature of thumbnails is the use
of a highlight block (e.g., red blocks in Fig. 3 )
to emphasize part of a visualization (e.g., [61]). Data
labels are added to emphasize a specific part of the data
that conveys the main point of the article (e.g., ‘46.0%’
and ‘39.8%’ of this thumbnail [55]). The Wall Street Journal
Graphics,FiveThirtyEight, and Bloomberg News frequently use
this thumbnail-design approach. In general, the visualiza-
tion in this type contains simpler axes than those in
thumbnails, excluding basic components such as axis labels
and gridlines. Reference lines can also have data labels,
which provide the information of the complete y-axis (e.g.,
the dotted vertical line and numbers in this thumbnail [55]).
thumbnails have recognizable objects, such as
logos or icons, on a plain line chart with grid lines as the
background. Their main feature is that both logos and icons
(e.g., icons of handcuffs [62]) directly refer to the data in the
visualization. (e.g., logos [54]). FiveThirtyEight and the Pew
Research Center frequently produce this type of thumbnail.
4.2 Workshop with Practitioners
To ensure the design quality of the designed thumbnails
and to collect feedback and expectations on the thumbnail
experiment, we held a workshop with 9 practitioners at a
data journalism conference. The practitioners were a data
scientist (five years of experience), three graphic designers
(six, four, and three years of experience, respectively), two
data journalists (five and four years of experience, respec-
tively), a computational journalist (three years of experi-
ence), and two interactive graphics developers (13 and three
years of experience, respectively). They all worked for data
journalism organizations.
As the workshop began, we described the four types
of visualization thumbnails and showed the 16 thumbnails
(Figure 3) produced with associated news articles. We asked
them to evaluate the quality of the thumbnails and their per-
sonal preferences for the thumbnail types. We also requested
that they report any possible issues that make a thumbnail
ineffective for use. Lastly, we asked them if they, based on
their domain experience, had comments, including possible
hypotheses or insights on the experiment.
Overall, we received positive feedback on the thumb-
nails; all practitioners agreed that there were no critical
issues that made the thumbnails look far different from
those they had created. For their preferences for thumbnails,
seven practitioners expressed their preference for , as
they are intuitive and eye-catching: I can easily catch that
what I would read ‘some statistics of Donald Trump’ and ‘the sales
of stock prices of two companies’." One practitioner expressed
her preference for thumbnails, pointing out both their
interpretability and appeal: News articles are similar to books
on a large shelf... The eye-catching design of a book is most
important. If the book cover is informative, it will do better."
Meanwhile, five practitioners stated they were interested in
our results because they assumed that readers could view
thumbnails as unfriendly, hard to read, and cluttered,
but they did not have evidence of this assumption.
To sum up, we found that the thumbnails we produced
are similar to what the practitioners create. We also con-
firmed that the thumbnails could be used in the user study
after revising them based on the collected design sugges-
tions (e.g., increasing font sizes, rounding line edges). The
practitioners were also curious about the readers’ evaluation
of the four types of thumbnails. Next, we describe how we
performed the user study with the designed thumbnails.
4.3 Study Procedure
For the experiment, we created a web page where read-
ers could browse news articles with thumbnails. For
the page layout, we referred to the graphics sections of
Bloomberg (https://www.bloomberg.com/graphics) and The
Wall Street Journal (https://graphics.wsj.com). For example,
the layout presented a news article thumbnail first and then
placed a title and bylines below the thumbnail. The web
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 8
GNRD
Highlight
4b
3a
Resized
1d
HRO
Fig. 4. Sample interface. Mobile page used for the experiment.
page listed four thumbnails at a time (Figure 4) and allowed
scrolling.
We conducted the user study with participants recruited
from Prolific. We allowed the participants to participate
in the experiment only once. We also prevented duplicate
participation by matching their platform ID (Prolific) to a
session key provided by the system to access our experi-
ment page. As participants entered the experiment website
published in Prolific, they were asked to read the introduc-
tion and electronically agree with the consent form. They
were also provided access to the IRB approval document
(UNIISTIRB-19-33-A). Then, they were asked to fill out a
demographic survey (e.g., gender, age, education, and fre-
quency of using a mobile device to read news articles). After
the survey, participants were asked to read instructions that
included the rules and restrictions for the experiment, such
as not to create new tabs or refresh the page.
After reading the instructions, the participants entered
the web page and clicked on an article that they wanted to
read. They were asked to write a reason for their choice (a
minimum of 100 characters). Participants were encouraged
to provide selection reasons for their choices and to avoid
writing reasons purely based on their personal preferences
on a particular topic/person/location (e.g., “I like to read
sports news more than other topics"). Finally, participants
were asked a consistency check question (e.g., “please click
the article that you chose”) and then end the session. The
latter was conducted to detect any crowdworkers who were
randomly clicking; since it was administered directly after
the original question, this was not a memory check, but
a consistency check. During the experiment, we recorded
the participants’ device types and sizes to ensure that only
mobile device users could participate. Participants did not
report any complaints about the difficulty or ambiguity of
the task instructions.
We recruited 237 participants and provided the same
compensation (£1.25, hourly wage rate: £9.86), regardless of
the result of the consistency check. However, we excluded
the data of 40 participants who did not show consistency in
their thumbnail selection. We also excluded the data of 36
participants who did not follow instructions for thumbnail
selection and chose articles based on their personal pref-
erences ( : 8, : 12, : 9, : 7). Specif-
ically, we excluded reasons that only mention personal
preferences without any opinion on the thumbnails. The
excluded reasons tend to only have expressions on the topic
(11 participants), article titles (4 participants), and charac-
ter/company (21 participants), such as “I chose this thumb-
nail because Apple is an incredibly popular company and it
is very concerning when they don’t do well financially.” We
also excluded reasons where coders were unable to reach
unanimous agreement due to ambiguity. For example, we
rejected this reason: “The data seems to run parallel in a
way that the other data did not. The consistency in the data
is very compelling," because coders could not agree which
part of the thumbnail the statement is describing. When
participants clearly mentioned the thumbnail image as the
reason for their selection, we included the data and coded
it accordingly. A “clear mention” includes elements such
as charts (and chart components), companies, and main
characters in the article.
In total, we analyzed the responses of 161 participants
(33 years old, on average, σ=9). They had moderate familiar-
ity with visualizations (1 = not familiar at all, 7 = common;
average score was 4) and frequently read news articles
on mobile devices: more than seven times per week: 64
participants; more than five times: 42; more than three times:
40; and less than three times: 15. During the experiment, the
participants used mobile devices with an average height and
width of 775.84 px (σ=101.45) and 388.40 px (min=320px,
max=450px, σ=32.18), respectively.
Before proceeding with our analysis, we first tested if
users differently choose thumbnails and found that par-
ticipants’ selections are significantly different (p < .05).
We then investigated if there is the ordering effect; if the
participants choose thumbnails in a specific position (e.g.,
always the first shown thumbnail). For this, we calculated
the probability of each position of thumbnails being selected
and divided this probability into four intervals from 0 to 1
(e.g., first: 0.3, second: 0.53, third: 0.8, and last:
1). Using a random function that selects X from 0 to 1,
the number of thumbnails selected according to random X is
determined according to the probability interval described
above. For example, if random X is 0.47, it falls into the
second interval, and we suppose that the thumbnail in the
second position in thumbnail combinations is selected in our
simulation. For the simulation, we used 161 sequences that
are used in the user study. We conducted this simulation
2,000 times and obtained a result that the selections were
not affected by any positions of the presented thumbnails
(p < .05). We uploaded the simulation code and procedure
in the supplementary materials (https://osf.io/khgw2/).
We also found very little interaction between the articles
and thumbnail types (Cramer’s V = 0.058) in participant
choices.
5 RESULTS AND ANALYSIS
We performed statistical tests to determine the most popular
thumbnail type and the reasons stated by participants for
their choice (RQ1). Then we conducted a qualitative analysis
that investigated the readers’ opinions on the components of
the visualization thumbnails (RQ2).
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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TABLE 4
Visualization thumbnail comment codebook. Table summarizing the selection reasons with comment counts. The light blue and yellow text
backgrounds indicate the interpretable and inviting categories, respectively.
Theme Counts
Informative 10 (23%)
9 (21%)Interesting, eye-catching
Storytelling 6 (14%)
Less cluttered 6 (14%)
Stimulating curiosity 5 (12%)
Professional, unbiased 5 (12%)
Aesthetically pleasing 2 (4%)
Matching with an article title 0 (0%)
Theme Counts
15 (29%)Interesting, eye-catching
11 (21%)Stimulating curiosity
6 (11%)Less cluttered
5 (10%)Storytelling
4 (8%)Aesthetically pleasing
4 (8%)Matching with an article title
1 (2%)Professional, unbiased
Theme Counts
Informative 24 (27%)
18 (20%)Interesting, eye-catching
16 (18%)Storytelling
11 (12%)Stimulating curiosity
5 (6%)Professional, unbiased
10 (11%)Less cluttered
3 (3%)Matching with an article title
2 (2%)Aesthetically pleasing
Theme Counts
Interesting, eye-catching 20 (26%)
19 (25%)Informative
12 (16%)Less cluttered
8 (10%)Storytelling
5 (6%)Stimulating curiosity
6 (8%)Professional, unbiased
5 (6%)Aesthetically pleasing
2 (3%)Matching with an article title
6 (11%)Informative
Interpretable (51%) Inviting (49%) Interpretable (40%) Inviting (60%) Interpretable (60%) Inviting (40%) Interpretable (47%) Inviting (53%)
Resized
43 comments
GNRD
52 comments
Highlight
89 comments
HRO
77 comments
5.1 Readers’ Choice for Thumbnail Types (RQ1)
We start by studying thumbnail type preference.
5.1.1 Most Chosen Thumbnail Type
First, our results showed significant preferences among the
four thumbnail types ( : 27, : 29, : 53, :
52) according to the chi-squared test (χ
2
(3, N=161, 14.975,
p=.0018).
To analyze the differences in users’ selection among
thumbnail types, we used a Thurstonian model [63], which
is a scaling method for converting people’s discrete selection
(e.g., a vote) into physical or mental concepts (e.g., partic-
ipants’ tendency levels on a chosen thumbnail compared
to others). The model assumes that individual rankings
are distributed around aggregate rankings. It also assumes
that a parameter exists based on the number of votes for
thumbnail , , , and and that the dis-
tribution of the values (i.e., preference) following Gaussian
can be estimated by probability. To quantitatively measure
the preference for thumbnail designs, we calculated the
posterior distributions of their choice using Monte Carlo
Markov-Chain (MCMC) [64], a popular method for approx-
imating a distribution through sampling.
To visualize the distribution, the frequency distribution
of the estimated thumbnail preference among the thumbnail
types, we first set ’s frequency distribution as zero
(A=0, a vertical black line in Figure 5) and sample 2000
times from the user selection data using the MCMC method.
Figure 5 shows the distributions (y-axis) of the estimated
thumbnail preference (x-axis) of , , and ,
compared to . The preference scale means that the
standard deviation of the preference is 1. So the effect size
(difference of mean/standard deviation) corresponding to
Cohen’s d is the distance itself. For example, 0.1318 is the
effect size. If two distributions of and another type
overlap, this means the probability of a reader choosing
one of the two types is close to 50%, respectively. As the
overlapped part between the two distributions becomes
narrow, the probability of a reader choosing becomes
lower (i.e., a thumbnail types on the right side of the x-axis
has a higher preference probability compared to ).
To find any significant difference in the preference
distributions for the thumbnail types, we calculated the
probability that the differences between the sampled
values from any two types are positive. When we picked
-0.15
0
10
-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3
20
30
40
50
60
70
80
A = 0 = 0.0135
C = 0.1373
D = 0.1318
B
C
D
Significantly
Different (<.05)
Significantly Different
Preference
Frequency
Resized GNRD
HRO
Highlight
GNRD
Highlight
HRO
Fig. 5. Participant preferences. Posterior distribution of participants’
preferences for thumbnail types shows that readers are likely to prefer
and thumbnails more than those from and .
two thumbnails from and , we found that
there is a 99.5% probability that the thumbnail from
has a higher reader preference than that from
(P
(GNRD<Highlig ht)
=99.5%). In the end, we found
that and thumbnails have significantly
higher reader preference probability distributions than
those of and (P
(Resized<Highlight)
=99.7%,
P
(Resized<HRO)
=99.7%, P
(GNRD<Highlig ht )
=99.5%, and
P
(GNRD<HRO)
=99.6%).
5.1.2 Thumbnail Selection Reasons
To analyze the reasons for thumbnail selection, we used
a bottom-up approach to thematic analysis [65]. For this
analysis, the lead author of this work coded the selection
reasons by developing low-level themes. Then, two other
authors reviewed the themes and revised them together to
see if there is any disagreement with the proposed themes.
Once all themes were established, the three authors inde-
pendently coded the data again with the themes and later
aggregated their results. The final agreement level of the
coders is 95.8% agreement (Fleiss’ Kappa = 0.26).
After building eight themes, we classified the themes
into two large categories, “inviting" and “interpretable",
which are the two main goals of visualization thumbnails
(Section 3.2). Table 5 summarizes the eight themes in two
categories, with exemplary comments. For inviting cate-
gories, we included themes related to drawing readers’
attention. For example, themes related to readers’ impres-
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 10
TABLE 5
Example comments. Comments have been grouped into eight themes
in the inviting and interpretable categories. Note that the study
participants are likely paying more attention to thumbnails than an
ordinary person would.
Category Theme Example
Informative (22%)
Storytelling (14%)
Interesting,
eye-catching (24%)
Stimulating
curiosity (13%)
Inviting
(48%)
Interpretable
(52%)
Aesthetically
pleasing (5%)
Matching with an
article title (3%)
“The graph seems very jittery and intense so it caught my eye..” (p160)
“I found the graph to be intriguing.” (p141)
“The fluctuations of the graph make me wonder about why the data is
so and what the article can say about it.” (P4)
“ the graph in the image made the most sense to me - the information
it was portraying seemed cleared.” (P73)
“It conveyed its Information in a simple and clear manner..” (p15)
“It is the least cluttered of the batch.” (p81)
Less cluttered
(13%)
“I found this graph to be the most visually compelling of the
thumbnails..” (p90)
Professional,
unbiased (6%)
“It lays the tone of professionalism.” (p29)
“It seemed to be the least misleading in its thumbnail graph.” (p80)
“It is the only graphic that seems to be telling a story right out of the
gate--at least one that I could start to make sense of..” (p13)
“The graphics and logo enforcing the articles headlines and made the
data more clear to understand.” (p115)
sions of a thumbnail, such as eye-catching, professional, or aes-
thetically pleasing, were classified into the inviting category.
For the interpretable category, we included themes related
to delivering articles’ content to readers. For example, less
cluttered belongs to the interpretable category because it
describes the thumbnails’ readability and visibility levels,
which are close to the method of information delivery. The
classification resulted in 43 comments for , 52 com-
ments for , 89 comments for , and 77 comments
for . Each participant reported 1.6 reasons for selection,
on average.
Table 4 shows the thematic coding results, where we
found that participants differently selected thumbnails with
different reasons, according to a chi-squared test– [ :
χ
2
(7, N=161, 14.116, p=.049], [ : χ
2
(7, N=161, 21.231,
p=.003], [
: χ
2
(7, N=161, 38.191, p=.0001], [ :
χ
2
(7, N=161, 33.026, p=.0001] Specifically, readers chose
and thumbnails because they are informa-
tive, interesting, and telling stories. On the other hand, they
picked the highlights in thumbnails as they provide
“stimulating curiosity." Since the main difference between
and thumbnails are in axes, highlights, and
data labels, we conjecture that the components have mainly
influenced readers’ final selection. Participants chose
and thumbnails due to their invitingness.
Reviewing all analysis results, we found the reasons that
readers prefer and thumbnails over and
thumbnails as follows. The low preference for
thumbnails was somewhat unexpected, as practitioners had
highly rated them due to their effective communication of
the articles’ main ideas without causing much visual clutter
(Section 4.2). However, this result implies that the ability
can be a double-edged sword, as it leads to low informa-
tiveness of the thumbnails, compared to other thumbnails,
as discussed in the following section.
5.2 Reader Opinions on Chart Components (RQ2)
We focus here on the axis information, data labels with high-
lights, and image components (GNRDs, HROs), as these are
the primary reasons for thumbnail selection and themes.
Abundant axes information may gain readers’ trust
but are rarely used for understanding data stories: One
characteristic of thumbnails is that they are the only
type of thumbnails that provide complete axes information
(e.g., axis labels and values). We first found that participants
welcome the detailed axis information in thumbnails
for an accurate understanding of data stories. Nine of 27
participants chose the axis information as to their selection
reason. P25 stated—“The clearly visible labels on the x- and y-
axis, [...] effective scale helped me quickly understand it." We also
noticed that thumbnails with full axes information might
help readers perceive trustworthiness and professionalism
from the articles (two of 27), as P19 states—“I can say that the
thumbnail is my pick because it lays the tone of professionalism."
P25 also expressed a similar opinion—“[...] most convincing
and less likely to be skewed due to the clearly visible labels on the
x and y axis."
However, participants did not use the axis information
extensively for interpretation. Instead, 10 of 27 participants
focused on overall line chart trends (e.g., P4: fluctuations of
the graph make me wonder [...]", P5: Apple is dropping [...]"),
which can be done quickly and easily. These interpretations
were not much different from the descriptions with
thumbnails, which were of interest, as they had an abstract
line with no axes (e.g., The graph seems to show more people
have grown [...]”). These participants’ thumbnail reading be-
havior can be viewed as a natural effort for quickly catching
the main idea of thumbnails without a waste of time and
in-depth analysis. This illustrates why thumbnails
do not attract readers’ preference—they present too much
information for the reading behavior people tend to employ
for visualization thumbnails.
Data labels with highlights can provide a visual
guide for readers: Highlights [18], [52], [53] and reference
lines [33], [66], [67] have been effective methods for at-
tracting readers and enhancing readers’ understandability
of data stories with visualization. We confirmed the effec-
tiveness of these techniques in thumbnails. For example,
reference lines (e.g., the black horizontal lines of
thumbnails in Figure 3) tend to make participants read data
stories in chronological order, working as anchors for the
data labels [33], [66], [67]. Nine of 53 participants described
how they understand the thumbnails with thumb-
nails, directly referring to the data labels—“I chose this article
(Article 2) [...] it was nearly a 50/50 split on January 23, 2017,
and on October 19, 2019 [...]," (P96). Such an increased under-
standing on the visualization could help participants better
infer the article’s context, as P76 describes—“[the thumbnail]
sets up the expectation the approval rating has dropped over the
last two years, so I basically know the premise of the article."
Combining multiple components can be a good strategy
to achieve both appeal and interpretability. For example, the
highlights combined with data labels can work as a good
visual guide" (P109), where highlights point where readers
need to focus and data labels stress what readers should
understand. The red blocks for presenting time ranges and
data labels not only drew readers’ attention to specific data
points but also made readers curious about the article’s
context (e.g., P71: The article shows a 41.2% approval rate
(in the red block), which is something I’d like to know more about,
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 11
i.e., the reasons").
GNRD and HRO, despite being considered eye-
grabbers, are associated with different keywords; GNRD
with “inspiration" and HRO with “informativeness": Im-
agery components, such as GNRDs and HROs, were effec-
tive in engaging readers and drawing their attention [18],
[20]. For thumbnails, 15 of 29 participants chose
GNRDs as their reason for the selection, because GNRDs
were much more visually engaging" (P49) and making the
thumbnail stand out more" (P41). Participants who chose
thumbnails also described HROs’ role as attention grabbers–
The inclusion of sports logos caught my eye" (P132), [...] logos
were recognizable and got my attention." (P152). We addition-
ally found a possibility that visualization thumbnails with
GNRDs could be more effective with a title in attracting
readers, which is the main access point for further read-
ing [2], [14], as P54 stated—“The photo caught my eye, which
leads to me reading the title."
Though both GNRDs and HROs attract readers, par-
ticipants’ interpretation of GNRDs and HROs may differ
in visualization thumbnails. In the study, participants with
HROs tend to intuitively understand the visualization, us-
ing HROs as visual legends" (P136). For example, in the
thumbnail for Trump in Figure 3, the thumbs up
or thumbs down icons represent approval or disapproval,
which are easy to understand as they are consistent with
text labels (i.e., ‘approve’ and ‘disapprove’)—“Thumbs up
and thumbs down is a universally used graphic represen-
tation which was easy to understand for me." This supports
previous work stating that HROs can convey representative
meanings in the form of icons or logos but also create data
redundancy by conveying meaning combined with text [27].
For GNRDs, incorporating human photos in charts could
be inspiring but may lead to open interpretations. Three par-
ticipants (3 of 29) believed that GNRDs effectively informed
the article’s main character–“This one stood out because it
had a picture of a person mentioned in the article [...] (with the
GNRD) easier to think about because you don’t need to visualize
the person mentioned." We also observed that the meanings
of human photos might be accepted differently by readers.
For example, with the same thumbnail ( thumbnail for
the stock prices), P31 commented that (The people in the
thumbnail) are selling Apple stock", while P35 reported that
[...] they were talking about and what they determine is a bad
month for Apple [...]". We think this can be aligned with the
previous finding that thumbnails with human photographs
construe interpersonal and textual meanings [68].
We conjecture that informativeness of chart components
is another important factor that affects the final selection
of thumbnails. For example, the participants may consider
thumbnails to be both attractive and informative, deliv-
ering the meaning of thumbnails in a straightforward man-
ner with text labels. GNRDs also attract readers’ attention,
but we find the possibility that the participants could inter-
pret the meaning of GNRDs somewhat differently. However,
given the small number of participants mentioning the
difference between GNRDs and HROs, there may be more
as-yet unknown effects of GNRDs.
Inviting Interpretable
Axis
Components
Chart Title
GNRDs
HROs
Highlights
Data Labels
+
Highlights
HROs
+
Implicit Legends
Data Labels
Implicit Legends
Fig. 6. Visualization thumbnail roles. Interpretability and invitation.
6 DISCUSSION
We have shown that readers want visualization thumbnails
that not only keep their eyes focused on the thumbnail (i.e.,
inviting), but also convey the article’s main point (informa-
tive). Our findings indicate that thumbnail design can be
aided by understanding chart component roles.
6.1 Lessons Learned and Design Implications
Figure 6 summarizes the role of chart components for vi-
sualization thumbnails. We place GNRDs, HROs, and high-
lights in the inviting category, while we place axes, implicit
legends, and data labels in the interpretable category. We
also find that data labels with highlights and HROs with
implicit legends can work for both inviting and interpretable
in the visualization thumbnails. We acknowledge that the
participants in our study are more likely to pay attention to
thumbnails than the general population. Here we provide
design suggestions that consider the roles of the chart com-
ponents and the combination of these components.
Lesson 1: Readers want easy-to-understand visualiza-
tion thumbnails. As thumbnails present stories, readers
expect to quickly and easily grasp the key information of
the story by viewing the thumbnails. As such, we think that
summarized and simple charts in thumbnails are preferable
compared to detailed charts, as shown in our results; readers
click thumbnails with brief summaries ( , 58) rather
than full charts for the articles ( , 27). We addition-
ally think that explicit and concise keywords can play an
important role in presenting the main point of the story,
while maintaining simple visualization thumbnails—“The
graph in the thumbnail is easier to look at. The large text makes it
a lot clearer what it’s supposed to be about at a glance compared
to the others,” as P72 states. Chart axes can aid readers in
understanding the thumbnail, but they may not be effective
if readers do not award them sufficient attention.
Suggestion 1: Including descriptive text with a summarized
visualization can make a thumbnail more understandable. We
notice that chart titles and data labels help readers to
grasp an article’s key messages, making a thumbnail more
interpretable (blue part in Figure 6). Chart titles should
summarize the entire chart to help readers get a quick
and intuitive understanding on the thumbnails. Data labels
of numbers can be critical design points that help readers
access the detailed but concise information of the article.
Lesson 2: Readers want attractive visualization thumb-
nails. Conventionally, eye-catching has been an important
factor in thumbnail design [3], [9], [10], [68], [69]. We confirm
in this work that readers also expect to see a thumbnail
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 12
that is attractive enough to draw their attention and invite
them for further clicks. In particular, we find that readers
prefer thumbnails with attractive chart components. Many
readers first catch images or highlights in thumbnails and
then become interested enough to explore the data and
read the article—“The red block made the thumbnail look more
entertaining to learn about" (P92).
Suggestion 2: Including highlights or visual embellishments
can make a visualization thumbnail engaging. Incorporating
highlights or images in thumbnails is a promising strategy
for drawing the reader’s attention to the thumbnail (i.e.,
the yellow part of Figure 6). Highlights make a part of the
chart stand out among other components and draw readers’
attention to the story. Visual embellishments such as GNRDs
and HROs can be also used for eye-catching purposes, but
we suggest that using them with cautions because it is
possible that they can mislead readers. For example, por-
trait photography (GNRDs) can bring unexpected meaning
when interpreted with given chart in a thumbnail.
6.2 Limitations and Open Research Areas
Although revealing readers’ thoughts on inviting and in-
terpretable visualization thumbnails throughout the user
study are the first attempt at visualization thumbnail design,
there are limitations in this work. In this section, we discuss
the limitations of this work and present future research
questions beyond this work.
Exploring the role of chart components from quali-
tative analysis: We investigated readers’ opinion on chart
components and their roles (RQ2) providing other aspects
of visualization thumbnails that previous studies did not
point out. For example, we suggested the different roles of
HROs and GNRDs in visualization thumbnails which have
not been considered thus far. Though the obtained results
are meaningful in the sense that they are the first responses
extracted from the readers, the result on the effectiveness
and interpretation of the chart components should not be
considered as definitive yet due to the limited number of
participants and extrapolations. We think a future study
could use the findings in this work in selecting chart compo-
nents to verify the specific role of the selected components
and their combinations. In this work, we focus on exploring
the role and effectiveness of different components in thumb-
nails. We think that a future study could investigate the role
of the components with consideration of topic, person, and
location perspectives.
Confirming prior work: As the first work for inves-
tigating readers’ preference and thoughts on visualization
thumbnail designs, we provided a series of findings, and
insights, and lessons from different analytical perspectives.
However, when compared to prior work, our findings may
be considered limited in that mostly confirm existing find-
ings. We think a future study may additionally validate
our hypotheses by focusing on the differences among other
types of thumbnails with different modification strategies
based on our work.
Different visualization thumbnail sizes: Different read-
ers may have different thumbnail size preferences and fa-
miliarity. As our work is the first study on visualization
thumbnail design, we used a single thumbnail size based
on our survey. Thus, the effect of thumbnail size remains
unanswered. A future study may investigate the possible
effects of different (smaller) visualization thumbnail sizes on
different computing platforms. This may implies there is a
space for testing various thumbnail sizes including different
size of components and fonts, as we described the roles
of effect of axes in Section 5.2. Our survey results show a
variety of sizes being used in online news media.
Presenting one message vs. summarizing a full story:
Designers can choose a message presentation strategy be-
tween conveying one fact/perspective or summarizing a
data story’s full narrative with a visualization thumbnail.
Among the visualization thumbnails used in the exper-
iments, we conjecture that the thumbnails seem
to support the story summary strategy by presenting a
baseline story with the time-series data and exposing the
main point with the red highlight and data label. This
can be related to picture superiority effect, in which image
content is more likely to lead to an increased understanding
of the material [70], [71], [72], [73], [74], [75], [76]. As we
did not consider the message presentation strategy in our
thumbnail design, there exist many questions on the matter.
For example, putting multiple or collage visualizations (e.g.,
bar+line, bar, and line) in one visualization thumbnail can
also be considered as a message presentation strategy, a
topic that can be further explored in future studies.
Design space generalization: In this work, we designed
and conducted a user study with visualization thumbnails
that present line charts inside them. As such, some insights
derived from this work may not be directly applicable to
other types of charts (e.g., maps) with different component
sizes, such as font sizes. For example, we described the
roles and effect of axes in Section 5.2, but further research
is needed to investigate whether the same effect occurs
with other chart types, such as bar chart, bubble chart,
or scatterplots. We used two editing types in this work—
resized and modified thumbnails—but a future study could
investigate other modification strategies, such as cropped
thumbnails. Additional experiments can be performed for
investigating other possible impacts, such as the impacts of
interactions (e.g., scrolling) and surrounding colors. Lastly,
we used two time-series in this study because we wanted
to understand how readers understand implicit labels and
HROs in situations involving more than one time-series.
We were concerned that using more than two time-series
could lead to overly complicated experiments given the
lack of prior work or guidelines. Nevertheless, conducting
experiments with more than two time-series is an interesting
extension for future work, and could shed light on addi-
tional effective thumbnail designs.
Different highlighting techniques: To investigate the
impact of the presence of highlights in visualization thumb-
nails, we chose highlights that are frequently seen in the
thumbnail designs (e.g., red block, reference lines). How-
ever, other highlighting techniques could show better per-
formance. Examples include changing opacity or colors [30],
[33] and adding graphical annotations (e.g., circles) [33],
[77]. Future work should investigate the role of the highlight
techniques on readers’ perception.
Impact of stimulating portrait photography (GNRDs):
In this work, we used photos that have neutral impressions
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 13
and gestures in an effort to prevent any possible biases
from any other features related to charts (Section 4.1). With
the photos, we find that portrait photography not only
catches readers’ attention but also tells a story when used
in visualization thumbnails, extending the previous works
[68], [69], [78], [79]. However, this result might not apply
to all other thumbnails, as there are many design choices
for photo selection in real-world thumbnails(e.g., a smiling
or frowning face). Future work could investigate the impact
of stimulating photos (GNRDs) with different features. For
example, we can study readers’ attention on various face
expressions of photography in visualization thumbnails,
compared to other components, such as headline texts.
Measuring a visualization thumbnail’s helpfulness af-
ter reading an article: In this work, we asked readers to
choose an article to read solely based on thumbnails. Thus,
we did not measure the perceived usefulness of visualiza-
tion thumbnails. Measuring the perceived helpfulness [2]
of visualization thumbnails is an important issue, as it
determines news organizations’ trust and reliability. For
example, after a reader reads an article, but the content
or conclusion of the article is not what is expected from
the thumbnail, the reader may skip thumbnails from the
news organizations. But what kind of factors or components
involves the usefulness of the thumbnails is not clear. Our
study indicates that GNRDs are one candidate that can affect
helpfulness of thumbnails, as they can be either informative
or misleading. As such, a future study needs to investigate
the perceived usefulness of visualization thumbnails and
the factors involved with the usefulness.
Impact of aesthetically-pleasing, artistic visualization
thumbnails: The visual aesthetics of visualization have
been discussed from many perspectives, including subjec-
tive impressions of visualization and design criteria [38],
[46]. In our study, the practitioners stated that they put much
effort on producing visually appealing thumbnails (sub-
section 3.2). We also observed many artistic visualization
thumbnails from news organizations. For example, FiveThir-
tyEight, the Wall Street Journal Graphics, and the Pudding tend
to adjust the color and contrast of photos (GNRDs) and
incorporate them into thumbnails together. However, due
to the larger design space and lack of clear criteria for de-
signing visually pleasing visualization thumbnails, we did
not discuss the topic in this work. For example, to design a
visually pleasing visualization thumbnail, one may need to
consider how to control biases from colorful backgrounds,
unique layouts, and face photos with different facial ex-
pressions and gestures. Future research could investigate
the aesthetics design space of visualization thumbnails and
answer what makes visualization thumbnails visually ap-
pealing to readers.
Honesty of responses: In the experiment, we followed
current best practice in crowdsourcing—we paid partici-
pants a sufficient and fair amount of compensation and
did not give any systemic clue that could lead them to
certain answers. However, we cannot be sure of the hon-
esty of participant responses. On the other hand, we have
not found any indication that participants lied about their
answers. Furthermore, we believe that in our experimental
design, honesty is less effort than dishonesty for participants
because that means they don’t have to put effort into fabri-
cating dishonest answers and reasons.
Potential ordering effects: We used randomization
rather than counterbalancing, so there is a potential of an
ordering effect impacting our findings. To find any pos-
sible ordering effect on thumbnail or article presentation
orders, we conducted a user thumbnail selection simulation
2,000 times as described in Section 4.3, where participants
choose thumbnails based on the selection probability in the
study. Thumbnails can be chosen in any position during the
simulations. Our findings from the simulation show that
participants were significantly unlikely to be affected by
thumbnail positions.
7 CONCLUSION
We began this project by asking what makes thumbnails
for data stories inviting and interpretable. We surveyed
visualization thumbnails and had a series of interviews with
practitioners about the design of thumbnails for data-driven
stories. Based on our survey, we defined the design types
of visualization thumbnails and conducted a user study
to determine the most appealing thumbnail design. Our
study results reveal a design space for thumbnails: a set of
thumbnail design guidelines that can be leveraged to attract
readers and help them understand the context of articles
associated with thumbnails. The results also indicate that
chart components are the keys in visualization thumbnails
to attract readers’ attention and enhance readers’ under-
standability of the visualization thumbnails. We also report
various thumbnail design strategies by effectively combin-
ing the chart components, such as a data summary with
highlights and data labels and a visual legend with text
labels and HROs. Ultimately, our study sheds light on an
uncharted design space for visualization thumbnail design
and toward automatically generating or recommending an
ideal set of visualization components to include in a thumb-
nail.
ACKNOWLEDGMENTS
This work was supported by the Korean National Re-
search Foundation (NRF) grant (No. 2021R1A2C1004542),
by a grant of the Korea Health Technology R&D Project
through the Korea Health Industry Development Institute
(KHIDI), funded by the Ministry of Health & Welfare,
Republic of Korea (grant number:HI22C0646), and by the In-
stitute of Information & Communications Technology Plan-
ning&Evaluation (IITP) grants (No.2020-0-01336–Artificial
Intelligence Graduate School Program, UNIST), funded by
the Korea government (MSIT).
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
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Hwiyeon Kim received her B.S. and M.S. degrees from UNIST (Ulsan
National Institute of Science and Technology) in Ulsan, South Korea in
2019 and 2021. Her research interests include data journalism and HCI.
Joohee Kim received her B.S. degree in Computer Science and Engi-
neering from UNIST (Ulsan National Institute of Science and Technol-
ogy) in Ulsan, South Korea. She is working toward her M.S. degree at
UNIST. Her research interests include data visualization and HCI.
Yunha Han received her M.S. in 2021 from UNIST (Ulsan National In-
stitute of Science and Technology) in Ulsan, South Korea. She is a data
engineer at NCSoft. Her research interests include data visualization
and HCI.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 16
Hwajung Hong received the Ph.D. degree from Georgia Institute of
Technology in 2015. She is an assistant professor in the Department
of Industrial Design at KAIST. Her research lies at the intersection
of human-computer interaction and journalism. Her research interests
include the social implications of data and artificial intelligence.
Oh-Sang Kwon received the doctoral degree in psychological sciences
at Purdue University in 2009. He is an associate professor in the
department of biomedical engineering at UNIST (Ulsan National Insti-
tute of Science and Technology) in Ulsan, South Korea. His research
interests include human visual perception, perceptual/cognitive biases,
perception-action interaction, and perceptual learning.
Young-Woo Park received the Ph.D. degree in industrial design in 2014
from KAIST in Daejeon, South Korea. He is an associate professor of
design at UNIST (Ulsan National Institute of Science and Technology)
in Ulsan, South Korea. As a director of the Interactive Product Design
Laboratory, his research highlights the significance of ’physical richness’
during interaction with technologies. He explores the potential of embod-
iment of tactile channel, shape-changing and data materialization as a
method for designing aesthetic interaction.
Niklas Elmqvist received the Ph.D. degree in
2006 from Chalmers University of Technology in
Göteborg, Sweden. He is a full professor in the
College of Information Studies at University of
Maryland, College Park in College Park, Mary-
land. He is also a member of the Institute for
Advanced Computer Studies (UMIACS) and for-
merly director of the Human-Computer Interac-
tion Laboratory (HCIL) at University of Maryland.
He is a senior member of the IEEE and the IEEE
Computer Society.
Sungahn Ko received the doctoral degree in
electrical and computer engineering in 2014
from Purdue University in West Lafayette, IN,
USA. He is an associate professor in the school
of Computer Science and Engineering at UNIST
(Ulsan National Institute of Science and Technol-
ogy) in Ulsan, South Korea. His research inter-
ests include visual analytics, information visual-
ization, and Human-Computer Interaction.
Bum Chul Kwon received his M.S. and Ph.D.
in Industrial Engineering from Purdue Univer-
sity in 2010 and 2013, and his B.S. in Systems
Engineering from University of Virginia in 2008.
He is a research scientist at IBM Research. His
research area includes visual analytics, data vi-
sualization, human-computer interaction, health-
care, and machine learning. Prior to joining IBM
Research, he worked as postdoctoral researcher
at University of Konstanz, Germany.
This article has been accepted for publication in IEEE Transactions on Visualization and Computer Graphics. This is the author's version which has not been fully edited and
content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2023.3278304
© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See https://www.ieee.org/publications/rights/index.html for more information.
Authorized licensed use limited to: Hong Kong University of Science and Technology. Downloaded on July 19,2023 at 07:46:22 UTC from IEEE Xplore. Restrictions apply.